{"id":"https://openalex.org/W2964056922","doi":"https://doi.org/10.1109/vcip.2018.8698695","title":"Driving Maneuvers Prediction Based on Cognition-driven and Data-driven Method","display_name":"Driving Maneuvers Prediction Based on Cognition-driven and Data-driven Method","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2964056922","doi":"https://doi.org/10.1109/vcip.2018.8698695","mag":"2964056922"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2018.8698695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2018.8698695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100639990","display_name":"Dong Zhou","orcid":"https://orcid.org/0000-0003-1674-1885"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong Zhou","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006236325","display_name":"Huimin Ma","orcid":"https://orcid.org/0000-0001-5383-5667"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Ma","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108047157","display_name":"Yuhan Dong","orcid":"https://orcid.org/0000-0001-5275-1787"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhan Dong","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100639990"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.63909171,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.73353959,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7848346829414368},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7312659025192261},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6884683966636658},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.6610105633735657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49482887983322144},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.48187312483787537},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4790255129337311},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4759409427642822},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.4525423049926758},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4313294291496277},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4276980459690094},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.42606550455093384},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16508430242538452},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11107578873634338},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08648750185966492}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7848346829414368},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7312659025192261},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6884683966636658},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.6610105633735657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49482887983322144},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.48187312483787537},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4790255129337311},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4759409427642822},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.4525423049926758},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4313294291496277},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4276980459690094},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.42606550455093384},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16508430242538452},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11107578873634338},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08648750185966492},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip.2018.8698695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2018.8698695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1557496348","https://openalex.org/W1665851364","https://openalex.org/W1789187189","https://openalex.org/W1923404803","https://openalex.org/W1968719911","https://openalex.org/W2042929057","https://openalex.org/W2064675550","https://openalex.org/W2077012593","https://openalex.org/W2166880842","https://openalex.org/W2508429489","https://openalex.org/W2519106163","https://openalex.org/W2519250527","https://openalex.org/W2624162241","https://openalex.org/W2741066535","https://openalex.org/W2963692464","https://openalex.org/W6633360099","https://openalex.org/W6739690495"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4288108708","https://openalex.org/W4387163678","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W3173606726","https://openalex.org/W2904359912"],"abstract_inverted_index":{"Advanced":[0],"Driver":[1],"Assistance":[2],"Systems":[3],"(ADAS)":[4],"improve":[5],"driving":[6,25],"safety":[7],"significantly.":[8],"They":[9],"alert":[10],"drivers":[11],"from":[12],"unsafe":[13],"traffic":[14],"conditions":[15],"when":[16],"a":[17,48,64,79,99],"dangerous":[18],"maneuver":[19],"appears.":[20],"Traditional":[21],"methods":[22,36,131],"to":[23,37,47],"predict":[24],"maneuvers":[26],"are":[27],"mostly":[28],"based":[29],"on":[30,119],"data-driven":[31,75],"models":[32],"alone.":[33],"However,":[34],"existing":[35],"understand":[38],"the":[39,70,74,87,90,93,96,120,126],"driver's":[40],"intention":[41],"remain":[42],"an":[43],"ongoing":[44],"challenge":[45],"due":[46],"lack":[49],"of":[50,52,106],"intersection":[51],"human":[53,115],"cognition":[54],"and":[55,73,92,132],"data":[56,88,94],"analysis.":[57],"To":[58],"overcome":[59],"this":[60],"challenge,":[61],"we":[62],"propose":[63],"novel":[65],"method":[66,128],"that":[67,125],"combines":[68],"both":[69],"cognition-driven":[71],"model":[72,80,104],"model.":[76],"We":[77],"introduce":[78],"named":[81],"Cognitive":[82],"Fusion-RNN":[83],"(CF-RNN)":[84],"which":[85],"fuses":[86],"inside":[89],"vehicle":[91,97],"outside":[95],"in":[98],"cognitive":[100],"way.":[101],"The":[102],"CF-RNN":[103],"consists":[105],"two":[107],"Long":[108],"Short-Term":[109],"Memory":[110],"(LSTM)":[111],"branches":[112],"regulated":[113],"by":[114],"reaction":[116],"time.":[117],"Experiments":[118],"Brain4Cars":[121],"benchmark":[122],"dataset":[123],"demonstrate":[124],"proposed":[127],"outperforms":[129],"previous":[130],"achieves":[133],"state-of-the-art":[134],"performance.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}